The field of the invention is magnetic resonance imaging (“MRI”) methods and systems. More particularly, the invention relates to acquiring MRI data from which both bone and soft tissues can be imaged.
There are many clinical applications where it is desirable to produce medical images that enable bone and other tissues to be visualized and segmented. For example, whole head segmentation methods that include bone provide a basis for calculating attenuation and scatter correction maps for positron emission tomography (PET) imaging. The attenuation correction and scatter (AC) correction map is important for accurate PET image reconstruction and the location of dense tissues such as bone is most important. Accurate AC maps should not only include the skull but should also the other bones in the head that may scatter or attenuate the gamma photons. AC maps are usually derived from a computed tomography (CT) or PET data, but these imaging modalities have drawbacks, for example, CT imaging exposes the patient to ionizing radiation. Thus, it would be desirable to utilize an alternative imaging modality that does not require the use of ionizing radiation or administration of a radiotracer.
For example, accurate, detailed, and subject-specific head models that include muscle, bone, marrow, skin, and facial features can also allow for more accurate localization of brain activation as measured by EEG and MEG. Experimental data from healthy brains and from artificially induced dipoles in epileptic patients suggest inherent localization differences for electric potential versus magnetic field data. In these studies, however, it is difficult to separate the effect of errors in the forward solution from localization errors due to differences between EEG and MEG. Specifically, there are fundamental differences between the forward solution accuracy required by EEG and MEG, with MEG requiring a simpler model. Given sufficiently accurate forward models for both EEG and MEG, explicitly combining EEG and MEG provides more accurate activity estimates than either measure by itself. The construction of accurate and detailed head models is required to combine the data.
In optical imaging, detailed head models can allow activation patterns to be inferred from measured signals with improved accuracy. Due to the diffuse nature of the near-infrared photons that are used to sample the tissue, the spatial resolution of diffuse optical imaging is limited to roughly 1-2 cm in the adult human cortex near the skull. This low spatial resolution results in significant errors in the quantitative characterization of hemoglobin concentrations in, for example, cortex versus skull due to partial volume effects. The complex non-linear propagation of light through tissue results in a partial volume effect that does not produce a linear average of the sampled tissues. Using magnetic resonance (MR) based segmentation labels as a structural a priori data in the optical imaging inverse problem removes the partial volume averaging, and enables the quantification of hemoglobin concentrations within each tissue type.
To acquire such data, a magnetic resonance imaging (MRI) system is utilized. When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or “spins”, after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques. The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically proven pulse sequences and they also enable the development of new pulse sequences.
Bone position can been inferred from the surrounding tissue in MR images. However, in some applications, such as PET attenuation correction, all the bone in the head must be labeled and non-skull bones (lower jaw/vertebrae) can be difficult to disambiguate from the surrounding tissue. While an expert human anatomist may be able to infer the position of bone from conventional MR images, it is much harder to train an automated system to do the same. For example, such systems have been observed to label the air in the mouth as “bone” continuous with the teeth. To simply and robustly identify bone, it is best to acquire signal from the bone itself. Also, bone marrow can provide MR signal in conventional scans that may cause improper segmentation for narrow regions of the skull with relatively more marrow. In PET attenuation correction, mistaking air for bone greatly impacts the quality of the reconstructed images and could result in missed or spurious “hot spots”.
Methods are known for acquiring MR images of bone. One of these employs an ultrashort TE (UTE) pulse sequence, which is used to image substances with short T2 relaxation times, including bone. However, UTE pulse sequences are not suited for imaging substances with longer T2 relaxation times, such as the brain tissue. Traditional pulse sequences for imaging brain morphometry, such as MPRAGE and FLASH are also well known for distinguishing surrounding soft tissues, but they are insensitive to bone. Some have tried to utilize these two separate pulse sequences to acquire both the data from the bone and the tissue in consecutive imaging acquisitions. In multispectral morphometry, it is extremely important that all images align properly and that small details defining the edges of structures are well depicted. In such consecutive imaging acquisitions, this is particularly problematic using traditional techniques because bone images acquired using one pulse sequence may not properly register with soft tissue images acquired using a different pulse sequence. This is especially true when studying narrow structures such as the cerebral spinal fluid (CSF) outside the cortex, the skull, and the layers of fat and skin outside the skull, where it is critical that images identifying the different structures align properly
It would therefore be desirable to have a system and method for gathering structural information about bone and soft tissue that does not subject the patient to undesirable doses of radiation or radiotracers, is not plagued by overly complex modeling schemes, and is not subject to errors in registering sets of data corresponding to tissue and bone.